Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery

. Application of Genetic Programming to the discovery of empirical laws is often impaired by the huge size of the domains involved. In physical applications, dimensional analysis is a powerful way to trim out the size of these spaces This paper presents a way of enforcing dimensional constraints through formal grammars in the GP framework. As one major limitation for grammar-guided GP comes from the initialization procedure (how to find admissible and sufficiently diverse trees with a limited depth), an initialization procedure based on dynamic grammar pruning is proposed. The approach is validated on the problem of identification of a materials response to a mechanical test. 1 Introduction This paper investigates the use of Genetic Programming [Koz92] for Machine Discovery (MD), the automatic discovery of empirical laws. In the classical Machine Learning framework introduced in the seminal work of Langley [LSB83], MD systems are based on inductive heuristics combined with s..

Discriminative Power of Input Features in a Fuzzy Model

Abstract. In many modern data analysis scenarios the rst and most urgent task consists of reducing the redundancy in high dimensional in-put spaces. A method is presented that quanties the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is dened on the basis of the available fuzzy rules and the resulting possibilistic information gain, associated with the use of a given input dimension, characterizes the input feature’s discrimi-native power. Due to the low computational expenses derived from the use of a fuzzy model, the proposed possibilistic information gain gen-erates a simple and ecient algorithm for the reduction of the input dimensionality, even for high dimensional cases. As real-world example, the most informative electrocardiographic measures are detected for an arrhythmia classication problem.